Hedge Fund Indexes

The information about hedge funds is not publicly available or listed on any exchange. Investors have to obtain this information directly from the hedge funds or data provider specializing in collecting hedge fund data.

Many such hedge fund data providers also publish indexes. These indexes allow easy tracking of the performance of hedge funds and also comparison across different funds. These indexes track both hedge funds and fund of funds.

Many institutions publish hedge fund indexes. Some of them are listed below:

  • Specialized firms

  • Hedge Fund Research

  • Van Hedge

  • Hennessee

  • Greenwich

  • Banks

  • Crédit Suisse/Tremont indexes (Based on the TASS database managed by Tremont/Owned by Lipper)

  • ABN AMRO EurekaHedge

  • Index providers

  • MSCI

  • S&P

  • FTSE

  • Educational institutes

  • CISDM

  • EDHEC

Hedge fund indexes can be equal-weighted or asset-weighted (weighted by assets under management for each fund.)

Asset-weighted indexes provide a better representation of the funds performance, however, some indexes still use equal-weights because they do not want to disclose the size of the funds.

There are no guiding principles on how individual hedge funds are included in an index. Most indexes are constructed by selecting funds from their database. So, if an index has volunteered to be included in a database, it can become a part of the index. Due to information asymmetry, the performance reported by various hedge fund indexes can vary significantly.

Another category of indexes, called investable indexes, includes only those funds that an investor can actually buy and sell. Such funds are called open funds. A hedge fund needs to have certain characteristics to become eligible for inclusion in such an index, for example, readily accept new investments, no lock-up periods, among others.

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Data Science in Finance: 9-Book Bundle

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Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.